Remotely sensed forest mapping has become an important way to meet the increasing needs for forest-cover-associated data. However, accuracy for such products varies with the condition of forest ecosystem. In this paper, a support vector machine (SVM) classifier combined with autonomous endmember extraction technique was performed to improve the performance of machine learning in satellite land cover classification and percent tree cover mapping. For the study area, Pingnan County, Guangxi Zhuang Autonomous Region, China, that featured as a complex and fragmented subtropical forest habitat, the...
